GANs, designed by I. Goodfellow et al. in 2014 [1]}, are
deep generative models which aim to reproduce new data from a given
original training set. They are characterized by two deep neural
networks, Generator \(G\)  and Discriminator \(D\) , which
are trained alternatively. During the training, \(G\)  progressively
generates data similar to the real one, while \(D\)  increases the
probability of assigning the correct labels to both real and fake
data. Numerous improvements have been made since the initial proposal
and, in particular, GANs have achieved remarkable success in image
processing and generation, via variations such as Deep Convolutional
GAN (DCGAN) [2]}, Auxiliary Classifier GAN (ACGAN)
[3]}, Progressive GAN [4]}, etc.
